# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations

import itertools
import unittest
from functools import partial
from typing import Any

import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import TrtLayerAutoScanTest

import paddle.inference as paddle_infer


class TrtConvertAnchorGeneratorTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_configs(self):
        def generate_input1(batch, attrs: list[dict[str, Any]]):
            return np.random.random([batch, 3, 64, 64]).astype(np.float32)

        for (
            batch,
            anchor_sizes,
            aspect_ratios,
            variances,
            stride,
            offset,
        ) in itertools.product(
            [1, 2, 4],
            [[64.0, 128.0, 256.0, 512.0]],
            [[0.5, 1, 2], [0.4, 1.2, 3]],
            [
                [1.0, 1.0, 1.0, 1.0],
                [0.5, 1.0, 0.5, 1.0],
            ],
            [[16.0, 16.0], [16.0, 32.0]],
            [0.5, 0.8],
        ):
            dics = [
                {
                    "anchor_sizes": anchor_sizes,
                    "aspect_ratios": aspect_ratios,
                    "variances": variances,
                    "stride": stride,
                    "offset": offset,
                }
            ]

            ops_config = [
                {
                    "op_type": "anchor_generator",
                    "op_inputs": {"Input": ["input_data"]},
                    "op_outputs": {
                        "Anchors": ["output_anchors"],
                        "Variances": ["output_variances"],
                    },
                    "op_attrs": dics[0],
                }
            ]
            ops = self.generate_op_config(ops_config)

            program_config = ProgramConfig(
                ops=ops,
                weights={},
                inputs={
                    "input_data": TensorConfig(
                        data_gen=partial(generate_input1, batch, dics)
                    )
                },
                outputs=[
                    "output_anchors",
                    "output_variances",
                ],
            )

            yield program_config

    def generate_dynamic_shape(self, attrs):
        self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 64, 64]}
        self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]}
        self.dynamic_shape.opt_input_shape = {"input_data": [2, 3, 64, 64]}
        return self.dynamic_shape

    def sample_predictor_configs(
        self, program_config, run_pir=False
    ) -> tuple[paddle_infer.Config, list[int], float]:
        def clear_dynamic_shape():
            self.dynamic_shape.min_input_shape = {}
            self.dynamic_shape.max_input_shape = {}
            self.dynamic_shape.opt_input_shape = {}

        def generate_trt_nodes_num(attrs, dynamic_shape):
            if dynamic_shape:
                return 1, 3
            else:
                return 0, 4

        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]

        # for static_shape
        clear_dynamic_shape()
        if not run_pir:
            self.trt_param.precision = paddle_infer.PrecisionType.Float32
            program_config.set_input_type(np.float32)
            yield (
                self.create_inference_config(),
                generate_trt_nodes_num(attrs, False),
                1e-5,
            )
            self.trt_param.precision = paddle_infer.PrecisionType.Half
            # NOTE(tizheng): This config will fall back to paddle native OP,
            # which only supports FP32 input.
            program_config.set_input_type(np.float32)
            yield (
                self.create_inference_config(),
                generate_trt_nodes_num(attrs, False),
                1e-3,
            )

        # for dynamic_shape
        self.generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        yield (
            self.create_inference_config(),
            generate_trt_nodes_num(attrs, True),
            1e-5,
        )
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield (
            self.create_inference_config(),
            generate_trt_nodes_num(attrs, True),
            1e-3,
        )

    def test(self):
        self.run_test(run_pir=True)


if __name__ == "__main__":
    unittest.main()
